On Mon, Aug 03, 2009 at 05:57:29PM -0400, Tom Foutz wrote:
> I saw that there is delaunay tessalation as well in mayavi, which
> sparked my question.
> I am using NEURON, a software package which now uses python. I have a
> number of neuron cells which I need to populate inside a volume. I
> have written a script which will randomly populate points within a
> cube which is larger than the volume of interest. All I need to know
> now is whether or not that point is within the volume described by
> triangular meshes.
OK, but the interior part of the delaunay tessalation of a cloud of
points is, AFAIK, not garantied to be the convex hull. It does contain
it, I believe.
Here is some code using the vtk ProbeFilter to evaluate the data in a
delaunay interpolation. If you put ones on your mesh, what evaluates to
one is inside the Delaunay interpolation range, and what evaluates to 0
is outside.
HTH,
Gaël
from enthought.mayavi import mlab
from enthought.tvtk.api import tvtk
import numpy as np
from scipy import vectorize
n = 8
t = np.linspace(-np.pi, np.pi, n)
z = np.exp(1j*t)
x = z.real.copy()
y = z.imag.copy()
z = np.zeros_like(x)
triangles = [(0, i, i+1) for i in range(n)]
x = np.r_[0, x]
y = np.r_[0, y]
z = np.r_[1, z]
mlab.clf()
mesh = mlab.triangular_mesh(x, y, z, triangles,
scalars=np.ones_like(x),
opacity=0.5)
delaunay = mlab.pipeline.delaunay3d(mesh)
unstructured_grid = delaunay.outputs[0]
def probe_data(dataset, x, y, z):
""" Probe a vtk dataset at given points x, y, z.
Parameters
----------
dataset: vtk dataset
The dataset giving the data you are interested in
x: float or ndarray
The x position where you want to query the data
y: float or ndarray
The y position where you want to query the data
z: float or ndarray
The z position where you want to query the data
Returns
--------
The values of the scalar data at the given point.
"""
# XXX: Should be implemented for more than just the scalars
shape = x.shape
x = np.atleast_1d(x)
y = np.atleast_1d(y)
z = np.atleast_1d(z)
probe_data = mesh = tvtk.PolyData(points=np.c_[x.ravel(),
y.ravel(),
z.ravel()])
probe = tvtk.ProbeFilter()
probe.input = probe_data
probe.source = dataset
probe.update()
values = probe.output.point_data.scalars.to_array()
values = np.reshape(values, shape)
return values
x_, y_, z_ = np.mgrid[-1:1:30j, -1:1:30j, -1:1:30j]
labels = probe_data(unstructured_grid, x_, y_, z_)
src = mlab.pipeline.scalar_field(x_, y_, z_, labels, transparent=True)
#scp = mlab.pipeline.scalar_cut_plane(src)
scp = mlab.pipeline.scalar_cut_plane(src)
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